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Semantics-Driven Conversational Interfaces for Museum Chatbots

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Culture and Computing (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12215))

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Abstract

This work addresses the challenges of creating usable and personalized conversational interfaces for broad, yet applicable, domains that require user engagement and learning, such as museum chatbots. Whether the chatbots are standalone or coupled with virtual agents or real-life robots, the functional requirements for interaction that targets specific learning aspects would be expected to be more or less similar. This work reports on experimental semantics-driven conversational interface design for chatbots in museum settings, targeting visitors to converse about exhibits and learn information about their style, the artists, the era, and other aspects related to them. Depending on the semantics (presentation, learning, exploration), chatbot scenarios were designed and evaluated by participants in a formative evaluation. The evaluation show that user requirement perception manifests in expectations on the semantic level, instead of just the technical level. The results between the scenarios are compared to see how the semantics considered for the design transferred to the implementation and to the user perception.

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Authors and Affiliations

  1. University of the Peloponnese, Tripoli, Greece

    Dimitris Spiliotopoulos & Costas Vassilakis

  2. University of the Aegean, Lesvos, Greece

    Konstantinos Kotis

  3. University of Athens, Athens, Greece

    Dionisis Margaris

Authors
  1. Dimitris Spiliotopoulos
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  2. Konstantinos Kotis
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  3. Costas Vassilakis
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  4. Dionisis Margaris
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Correspondence to Dimitris Spiliotopoulos .

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  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Matthias Rauterberg

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Spiliotopoulos, D., Kotis, K., Vassilakis, C., Margaris, D. (2020). Semantics-Driven Conversational Interfaces for Museum Chatbots. In: Rauterberg, M. (eds) Culture and Computing. HCII 2020. Lecture Notes in Computer Science(), vol 12215. Springer, Cham. https://doi.org/10.1007/978-3-030-50267-6_20

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  • DOI: https://doi.org/10.1007/978-3-030-50267-6_20

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